# The Meaning Behind the Cross Validation Score in Factor Analysis

In order to choose the best number of underlying factors for my data using factor analysis, I decided to use the tutorial outlined in scikit-learn's documentation.

Running cross_val_score(fa, X) outputs a score (usually a negative number). What is this score actually measuring? Any references with your answer would be much appreciated!

## 1 Answer

The score you get is determined by the estimator you use. In your case this is Factor Analysis. Looking at the documentation of the algorithm, you can find the explanation:

FactorAnalysis performs a maximum likelihood estimate of the so-called loading matrix, the transformation of the latent variables to the observed ones, using expectation-maximization (EM).

and the reference:

score(X[, y])   Compute the average log-likelihood of the samples


So your number is the logarithm of the maximum likelihood estimate of the factor loading matrix.